Introduction

On December 31st, the World Health Organization (WHO) was alerted of a cluster of cases of pneumonia in Wuhan, Hubei Province. A novel coronavirus was eventually identified. This virus eventually spread to almost the entire world and has infected 11,032,173 people as of July 3rd, 2020. Out of these 11,032,173 cases, 6,184,991 have recovered and 525,120 died.

In this report, we will be analysing the Covid-19 cases from 01/22/2020 - 05/22/2020, the span of 122 days. The report is organized as follows:

1. Top 12 Countries with the Most Cases
2. Top Continents with the Most Cases
3. Cases in China
4. Cases in the United States of America
5. Cases in China vs USA
6. Cases in Malaysia & Prediction of Future Cases

Libraries used:

• Pandas, numpy – for data processing
• Sklearn – for machine learning and prediction
• Ipywidgets, Ipython.display – for graph and table presentation
• Plotly – for plotting graph and data visualization

####################################################### SKIP THIS CELL #######################################################

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import ipywidgets as widgets
import sklearn.model_selection
from datetime import datetime
from datetime import timedelta
from sklearn.svm import SVR
from IPython.display import display, HTML
from IPython.display import clear_output
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

Data Pre-processing

The dataset used is from this Github repository provided by the John Hopkins University and is updated everyday.

Data Selection

There is inconsistency in the dataset we used as there are different column names and numbers of columns throughout the whole dataset. Hence, we have performed some data selection and only select five columns of data, which are 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', and 'Recovered'. Active cases from the dataset are also filtered out as we will perform the data transformation ourselves. Besides data selection, we also tried to standardise column names and country names. We also did some minor data cleaning by removing the data obtained from the cruise ship (Diamond Princess & Grand Princess). The cleaned datasets are then hashed for future access.

## Data selection - create a clean dataset that only require 5 columns of  data

dates = pd.date_range(start="01-22-2020",end="05-22-2020").to_pydatetime()

col1 = "Province/State"
col2 = "Country/Region"
col3 = "Confirmed"
col4 = "Deaths"
col5 = "Recovered"
column_names = [col1, col2, col3, col4, col5]
cleanDataset = pd.DataFrame(columns = column_names)
fullCleanDataset = {}

for i in range(len(dates)):
    filePath = ("csse_covid_19_daily_reports\\%s.csv" % dates[i].strftime("%m-%d-%Y"))
    df = pd.read_csv(filePath)
    df = df.fillna(0)

    cleanDataset = pd.DataFrame(columns = column_names)

    ## standardised the column names
    if set([col1]).issubset(df.columns):
         cleanDataset[col1] = df[col1]
    else:
         cleanDataset[col1] = df["Province_State"]

    if set([col2]).issubset(df.columns):
         cleanDataset[col2] = df[col2]
    else:
         cleanDataset[col2] = df["Country_Region"]

    cleanDataset[col3] = df[col3]
    cleanDataset[col4] = df[col4]
    cleanDataset[col5] = df[col5]

    ## standardised the country names
    cleanDataset.loc[cleanDataset['Country/Region'] == 'Mainland China', 'Country/Region'] = 'China'
    cleanDataset.loc[cleanDataset['Country/Region'] == 'Iran (Islamic Republic of)', 'Country/Region'] = 'Iran'
    cleanDataset.loc[cleanDataset['Country/Region'] == 'Congo (Brazzaville)', 'Country/Region'] = 'Congo'
    cleanDataset.loc[cleanDataset['Country/Region'] == 'Congo (Kinshasa)', 'Country/Region'] = 'Congo'

    ## remove cases from cruise ship 
    cleanDataset.drop( cleanDataset[ cleanDataset['Province/State'] == 'Grand Princess' ].index , inplace=True)
    cleanDataset.drop( cleanDataset[ cleanDataset['Province/State'] == 'Diamond Princess' ].index , inplace=True)

    fullCleanDataset[dates[i].strftime("%m-%d-%Y")] = cleanDataset     
#     fullCleanDataset[i] = cleanDataset    # hash the databaset with index

Data Transformation

In this section, we performed data transformation based on the continent, country or state/province. We filtered the data according the region and then summed all the cases to obtain total confirmed cases, total deaths cases and total recovered cases. The daily numbers for confirmed cases, deaths cases and recovered cases are then calculated by finding the difference between two consecutive days. Finally, we computed the number of active cases by deducting the total confirmed cases with total deaths cases and total recovered cases.

## Data transformation - aggregating data
## Data cleaning - remove noises
def getCountry(fullCleanDataset, country):
    size = len(fullCleanDataset)
    summary = np.zeros((size, 7), dtype=np.int64)
    count = 0
    for key in fullCleanDataset:
        df = fullCleanDataset[key]
        df = df.loc[df['Country/Region'] == country]
        totalConfirmed = df.iloc[:,2].sum(axis=0)
        totalDeaths = df.iloc[:,3].sum(axis=0)
        totalRecovered = df.iloc[:,4].sum(axis=0)
        active = totalConfirmed -totalDeaths - totalRecovered
        
        if count == 0:
            newConfirmed = 0
            newDeaths = 0
            newRecovered = 0
        else:
            
            # Prevent total count from decreasing
            if totalConfirmed < summary[count-1, 0]:
                totalConfirmed = summary[count-1, 0]
            if totalDeaths < summary[count-1, 1]:
                totalDeaths = summary[count-1, 1]
            if totalRecovered < summary[count-1, 2]:
                totalRecovered = summary[count-1, 2]
            
            # getting new information
            newConfirmed = totalConfirmed - summary[count-1, 0]
            newDeaths = totalDeaths - summary[count-1, 1]
            newRecovered = totalRecovered - summary[count-1, 2]
        
        summary[count] =  [totalConfirmed, totalDeaths, totalRecovered, newConfirmed, newDeaths, newRecovered, active]
        count = count + 1
    return summary

def getContinent(fullCleanDataset, continent):
    size = len(fullCleanDataset)
    summary = np.zeros((size, 7), dtype=np.int64)
    count = 0
    for key in fullCleanDataset:
        df = fullCleanDataset[key]
        temp = df[0:0]

        for country in continent:
            countryDF = df.loc[df['Country/Region'] == country]
            temp = temp.append(countryDF)

        totalConfirmed = temp.iloc[:,2].sum(axis=0)
        totalDeaths = temp.iloc[:,3].sum(axis=0)
        totalRecovered = temp.iloc[:,4].sum(axis=0)
        active = totalConfirmed -totalDeaths - totalRecovered
        
        if count == 0:
            newConfirmed = 0
            newDeaths = 0
            newRecovered = 0
        else:
            
            # Prevent total count from decreasing
            if totalConfirmed < summary[count-1, 0]:
                totalConfirmed = summary[count-1, 0]
            if totalDeaths < summary[count-1, 1]:
                totalDeaths = summary[count-1, 1]
            if totalRecovered < summary[count-1, 2]:
                totalRecovered = summary[count-1, 2]
            
            # getting new information
            newConfirmed = totalConfirmed - summary[count-1, 0]
            newDeaths = totalDeaths - summary[count-1, 1]
            newRecovered = totalRecovered - summary[count-1, 2]
    
        summary[count] =  [totalConfirmed, totalDeaths, totalRecovered, newConfirmed, newDeaths, newRecovered, active]
        count = count + 1
    return summary

def getProvince(fullCleanDataset, province):
    size = len(fullCleanDataset)
    summary = np.zeros((size, 7), dtype=np.int64)
    count = 0
    for key in fullCleanDataset:
        df = fullCleanDataset[key]
        df = df.loc[df['Province/State'] == province]
        totalConfirmed = df.iloc[:,2].sum(axis=0)
        totalDeaths = df.iloc[:,3].sum(axis=0)
        totalRecovered = df.iloc[:,4].sum(axis=0)
        active = totalConfirmed -totalDeaths - totalRecovered
        
        if count == 0:
            newConfirmed = 0
            newDeaths = 0
            newRecovered = 0
        else:
            
            # Prevent total count from decreasing
            if totalConfirmed < summary[count-1, 0]:
                totalConfirmed = summary[count-1, 0]
            if totalDeaths < summary[count-1, 1]:
                totalDeaths = summary[count-1, 1]
            if totalRecovered < summary[count-1, 2]:
                totalRecovered = summary[count-1, 2]
            
            # getting new information
            newConfirmed = totalConfirmed - summary[count-1, 0]
            newDeaths = totalDeaths - summary[count-1, 1]
            newRecovered = totalRecovered - summary[count-1, 2]
        
        summary[count] =  [totalConfirmed, totalDeaths, totalRecovered, newConfirmed, newDeaths, newRecovered, active]
        count = count + 1
    return summary

def getState(fullCleanDataset, state):
    size = len(fullCleanDataset)
    summary = np.zeros((size, 7), dtype=np.int64)
    count = 0
    for key in fullCleanDataset:
        df = fullCleanDataset[key]
        df = df.loc[df['Province/State'] == state]
        totalConfirmed = df.iloc[:,2].sum(axis=0)
        totalDeaths = df.iloc[:,3].sum(axis=0)
        totalRecovered = df.iloc[:,4].sum(axis=0)
        active = totalConfirmed -totalDeaths - totalRecovered
        
        if count == 0:
            newConfirmed = 0
            newDeaths = 0
            newRecovered = 0
        else:
            
            # Prevent total count from decreasing
            if totalConfirmed < summary[count-1, 0]:
                totalConfirmed = summary[count-1, 0]
            if totalDeaths < summary[count-1, 1]:
                totalDeaths = summary[count-1, 1]
            if totalRecovered < summary[count-1, 2]:
                totalRecovered = summary[count-1, 2]
            
            # getting new information
            newConfirmed = totalConfirmed - summary[count-1, 0]
            newDeaths = totalDeaths - summary[count-1, 1]
            newRecovered = totalRecovered - summary[count-1, 2]
        
        summary[count] =  [totalConfirmed, totalDeaths, totalRecovered, newConfirmed, newDeaths, newRecovered, active]
        count = count + 1
    return summary

def convertToDF(dataset):
    colName = ["Total Confirmed", "Total Deaths", "Total Recovered", "Daily Confirmed", 
               "Daily Deaths", "Daily Recovered", "Active"]
    dates = pd.date_range(start="01-22-2020",periods=len(dataset)).to_pydatetime()
    for i in range(len(dataset)):
        dates[i] = dates[i].strftime("%m-%d-%Y")
    convert = pd.DataFrame(dataset, index=dates, columns=colName)

    return convert

Top Countries

In this section, we compare the top 12 countries with the most infected cases. These countries are US, Russia, Brazil, United Kingdowm, Spain, Italy, France, Germany, Turkey, Iran, China, India. These countries are selected from the dataset by using data selection techniques as shown below.

####################################################### SKIP THIS CELL #######################################################

## getting data for countries
top_countries = ['US', 'Russia', 'Brazil', 'United Kingdom', 'Spain', 'Italy', 'France', 'Germany', 'Turkey', 'Iran', 
                 'China', 'India']
top_countries_DB = {}

for country in top_countries:
    top_countries_DB[country] = getCountry(fullCleanDataset, country)

################################################################################################################################

## dropdown for statistic
summaryDD = widgets.Dropdown(
    options = top_countries,
    description = 'Country: ',
)

def on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(summaryDD)
        df = getCountry(fullCleanDataset, change['new'])
        df = convertToDF(df)
        display(df.describe())

summaryDD.observe(on_change)

################################################################################################################################

## function
def plotGraph(fig, db, list, index, chart):
    if chart == 'lines':
        for element in list:
            addTrace(fig, element, db[element], index, chart)
    elif chart == 'bar':
        count = 0
        for element in list:
            if count == 0:
                addTrace(fig, element, db[element], index, chart)
            else:
                addTrace(fig, element, db[element], index, chart, 'legendonly')
            count = count + 1


## Initialise all figures and corresponding dropdowns
x_dates = pd.date_range(start="01-22-2020",end="05-22-2020").to_pydatetime()
for i in range(len(x_dates)):
    x_dates[i] = x_dates[i].strftime("%b %d")

def addTrace(fig, country, dataset, index, chart, visible=True):
    if chart != 'bar':
        fig.add_trace(go.Scatter(
            name = country,
            x = x_dates,
            y = dataset[:, index],
            mode = chart,
        ))
    else:
        fig.add_trace(go.Bar(
            name = country,
            x = x_dates,
            y = dataset[:, index],
            visible = visible,
        ))

        
figDD1 = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(figDD1)
        fig1.update_yaxes(type = change['new'])
        fig1.show()
figDD1.observe(fig_on_change)

fig1 = go.Figure()
plotGraph(fig1, top_countries_DB, top_countries, 0, 'lines')
fig1.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig1.update_layout(
    title = "Total Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)


fig2 = go.Figure()
plotGraph(fig2, top_countries_DB, top_countries, 3, 'bar')
fig2.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig2.update_layout(
    title = "Daily Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Confirmed Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)



fig3DD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig3_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(fig3DD)
        fig3.update_yaxes(type = change['new'])
        fig3.show()
fig3DD.observe(fig3_on_change)

fig3 = go.Figure()
plotGraph(fig3, top_countries_DB, top_countries, 1, 'lines')
fig3.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig3.update_layout(
    title = "Total Deaths Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Deaths Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)


fig4 = go.Figure()
plotGraph(fig4, top_countries_DB, top_countries, 4, 'bar')
fig4.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig4.update_layout(
    title = "Daily Deaths Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Deaths Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)


fig5DD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig5_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(fig5DD)
        fig5.update_yaxes(type = change['new'])
        fig5.show()
fig5DD.observe(fig5_on_change)

fig5 = go.Figure()
plotGraph(fig5, top_countries_DB, top_countries, 2, 'lines')
fig5.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig5.update_layout(
    title = "Total Recovered Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Recovered Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig6 = go.Figure()
plotGraph(fig6, top_countries_DB, top_countries, 5, 'bar')
fig6.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig6.update_layout(
    title = "Daily Recovered Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Recovered Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)



fig7 = go.Figure()
plotGraph(fig7, top_countries_DB, top_countries, 6, 'lines')
fig7.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig7.update_layout(
    title = "Active Cases",
    xaxis_title = "Dates",
    yaxis_title = "Active Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)


display()

Statistical Data

Please select different countries in the toggle to view different statistics. However, the descriptive statistics for total confirmed cases, total deaths cases and total recovered cases does not provide valuable information as they are aggregrated data.

display(summaryDD)
df = getCountry(fullCleanDataset, 'US')
df = convertToDF(df)
display(df.describe())

	Total Confirmed	Total Deaths	Total Recovered	Daily Confirmed	Daily Deaths	Daily Recovered	Active
count	1.220000e+02	122.000000	122.000000	122.000000	122.000000	122.000000	1.220000e+02
mean	4.137154e+05	22684.081967	56877.590164	13121.180328	786.688525	2869.959016	3.341766e+05
std	5.374458e+05	32251.239279	91754.100269	13293.424129	977.359396	6477.196703	4.182075e+05
min	1.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000e+00
25%	3.500000e+01	0.000000	5.000000	2.000000	0.000000	0.000000	3.000000e+01
50%	3.867750e+04	488.500000	171.000000	9072.000000	122.000000	18.500000	3.818900e+04
75%	8.329792e+05	46077.500000	76825.500000	26161.000000	1518.000000	3571.250000	7.100762e+05
max	1.600785e+06	95976.000000	350135.000000	36163.000000	4591.000000	51717.000000	1.183878e+06

Total Confirmed Cases

This section displays the total number of confirmed cases. To view different types of graph, choose toggle the dropdown. To view different countries, please click on the names. This is an interactive graph.

display(figDD1)
fig1.show()

Out of the 12 countries displayed, US takes the lead in numbers although the US had sightings of cases around the same time as China. While China put in measures quickly to stop the spread of the virus, the US placed no precautionary measures to restrict movement and contain the virus. By March, China had successfully flattened the curve, while other countries were just beginning to experience the beginning of the peak. Russia and Brazil have been hit particularly hard because of the lack of movement restriction lockdown measures put in place before clusters began to form in the respective countries. Even as the number of cases in these countries were steadily growing, the WHO advised them to omit restriction lockdowns and to have faith in the discipline of their people. Clearly, this did not work. For other countries such as France, Italy and Germany, their medical supplies and services were hit hard as they went into country-wide lockdown, and it seemed that the curve has been flattened since the start of early May.

Daily Confirmed Cases

This section displays the daily number of confirmed cases. To view different countries, please click on the names. This is an interactive graph.

fig2.show()

Total Deaths Cases

This section displays the total number of death cases. To view different types of graph, choose toggle the dropdown. To view different countries, please click on the names. This is an interactive graph.

display(fig3DD)
fig3.show()

Daily Deaths Cases

This section displays the daily number of death cases. To view different countries, please click on the names. This is an interactive graph.

fig4.show()

Total Recovered Cases

This section displays the total number of recovered cases. To view different types of graph, choose toggle the dropdown. To view different countries, please click on the names. This is an interactive graph.

display(fig5DD)
fig5.show()

Recovery in the US started a little later than the rest of the countries shown because they were late to respond to the pandemic. As expected, China had already flattened the curve before the rest of the countries had even shown signs of recovery.

Daily Recovered Cases

This section displays the daily number of recovered cases. To view different countries, please click on the names. This is an interactive graph.

fig6.show()

Active Cases

This section displays the total number of active cases. To view different countries, please click on the names. This is an interactive graph.

fig7.show()

Continents

In this section, we compare the number of cases by continents. The continents are split into Asia, Africa, Europe, North America, South America and Ocenia. The data was obtained from this website.

####################################################### SKIP THIS CELL #######################################################

## getting data for continents
continents = ['Asia', 'Africa', 'Europe', 'North America', 'South America', 'Oceania']

Asia = ['Afghanistan', 'Bahrain', 'Bangladesh', 'Bhutan', 'Brunei', 'Burma', 'Cambodia', 'China', 'Hong Kong'
        'East Timor', 'India', 'Indonesia', 'Iran', 'Iraq', 'Israel', 'Japan', 'Jordan', 'Kazakhstan', 'Korea, North', 
        'Korea, South', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Lebanon', 'Malaysia', 'Maldives', 'Mongolia', 'Nepal', 'Oman', 
        'Pakistan', 'Philippines', 'Qatar', 'Russian Federation', 'Saudi Arabia', 'Singapore', 'Sri Lanka', 'Syria', 
        'Taiwan', 'Tajikistan', 'Thailand', 'Turkey', 'Turkmenistan', 'United Arab Emirates', 'Uzbekistan', 'Vietnam', 'Yemen']

Africa = ['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Burundi', 'Cameroon', 'Cape Verde', 
          'Central African Republic', 'Chad', 'Comoros', 'Congo', 'Djibouti', 'Egypt', 'Equatorial Guinea', 'Eritrea', 
          'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Ivory Coast', 'Kenya', 'Lesotho', 'Liberia', 
          'Libya', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia', 'Niger', 
          'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal', 'Seychelles', 'Sierra Leone', 'Somalia', 'South Africa', 
          'South Sudan', 'Sudan', 'Swaziland', 'Tanzania', 'Togo', 'Tunisia', 'Uganda', 'Zambia', 'Zimbabwe']

Europe = ['Albania', 'Andorra', 'Armenia', 'Austria', 'Azerbaijan', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria',
          'Croatia', 'Cyprus', 'CZ', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 
          'Iceland', 'Ireland', 'Italy', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia', 'Malta', 'Moldova',
          'Monaco', 'Montenegro', 'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania', 'San Marino', 'Serbia', 'Slovakia',
          'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom', 'Vatican City']

NorthAmerica = ['Antigua and Barbuda', 'Bahamas', 'Barbados', 'Belize', 'Canada', 'Costa Rica', 'Cuba', 'Dominica', 
                'Dominican Republic','El Salvador', 'Grenada', 'Guatemala', 'Haiti', 'Honduras', 'Jamaica', 'Mexico', 
                'Nicaragua', 'Panama', 'Saint Kitts and Nevis','Saint Lucia', 'Saint Vincent and the Grenadines', 
                'Trinidad and Tobago', 'US']

SouthAmerica = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Peru', 'Suriname', 
                'Uruguay', 'Venezuela']

Oceania = ['Australia', 'Fiji', 'Kiribati', 'Marshall Islands', 'Micronesia', 'Nauru', 'New Zealand', 'Palau', 
           'Papua New Guinea', 'Samoa', 'Solomon Islands', 'Tonga', 'Tuvalu', 'Vanuatu']

continentsHash = {'Asia': Asia, 'Africa': Africa, 'Europe': Europe, 'North America': NorthAmerica, 
                  'South America': SouthAmerica, 'Oceania': Oceania }

continents_DB = {}
for continent in continents:
    continents_DB[continent] = getContinent(fullCleanDataset, continentsHash[continent])

asia = getContinent(fullCleanDataset, Asia)
africa = getContinent(fullCleanDataset, Africa)
europe = getContinent(fullCleanDataset, Europe)
northAmerica = getContinent(fullCleanDataset, NorthAmerica)
southAmerica = getContinent(fullCleanDataset, SouthAmerica)
oceania = getContinent(fullCleanDataset, Oceania)

################################################################################################################################

## dropdown for statistic
# continents = ['Asia', 'Africa', 'Europe', 'North America', 'South America', 'Oceania']
# continentsHash = {'Asia': asia, 'Africa': africa, 'Europe': europe, 'North America': northAmerica, 
#                   'South America': southAmerica, 'Oceania': oceania }

summaryDD2 = widgets.Dropdown(
    options = continents,
    description = 'Continent: ',
)

def on_change2(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(summaryDD2)
        df = continents_DB[change['new']]
        df = convertToDF(df)
        display(df.describe())
        
summaryDD2.observe(on_change2)

################################################################################################################################

## Initialise all figures and corresponding dropdowns
fig1bDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig1b_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(fig1bDD)
        fig1b.update_yaxes(type = change['new'])
        fig1b.show()
fig1bDD.observe(fig1b_on_change)

fig1b = go.Figure()
plotGraph(fig1b, continents_DB, continents, 0, 'lines')
fig1b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig1b.update_layout(
    title = "Total Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)



fig2b = go.Figure()
plotGraph(fig2b, continents_DB, continents, 3, 'bar')
fig2b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig2b.update_layout(
    title = "Daily Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Confirmed Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig3bDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig3b_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(fig3bDD)
        fig3b.update_yaxes(type = change['new'])
        fig3b.show()
fig3bDD.observe(fig3b_on_change)

fig3b = go.Figure()
plotGraph(fig3b, continents_DB, continents, 1, 'lines')
fig3b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig3b.update_layout(
    title = "Total Deaths Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Deaths Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig4b = go.Figure()
plotGraph(fig4b, continents_DB, continents, 4, 'bar')
fig4b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig4b.update_layout(
    title = "Daily Deaths Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Deaths Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig5bDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)
def fig5b_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(fig5bDD)
        fig5b.update_yaxes(type = change['new'])
        fig5b.show()
fig5bDD.observe(fig5b_on_change)

fig5b = go.Figure()
plotGraph(fig5b, continents_DB, continents, 2, 'lines')
fig5b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig5b.update_layout(
    title = "Total Recovered Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Recovered Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)


fig6b = go.Figure()
plotGraph(fig6b, continents_DB, continents, 5, 'bar')
fig6b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig6b.update_layout(
    title = "Daily Recovered Cases",
    xaxis_title = "Dates",
    yaxis_title = "Daily Recovered Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig7b = go.Figure()
plotGraph(fig7b, continents_DB, continents, 6, 'lines')
fig7b.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig7b.update_layout(
    title = "Active Cases",
    xaxis_title = "Dates",
    yaxis_title = "Active Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

display()

Statistical Data

Please select different continents in the toggle to view different statistics. However, the descriptive statistics for total confirmed cases, total deaths cases and total recovered cases does not provide valuable information as they are aggregrated data.

display(summaryDD2)
df = continents_DB['Asia']
df = convertToDF(df)
display(df.describe())

	Total Confirmed	Total Deaths	Total Recovered	Daily Confirmed	Daily Deaths	Daily Recovered	Active
count	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000
mean	251726.557377	8770.401639	132953.311475	7373.008197	217.221311	4302.155738	110002.844262
std	248817.768302	8179.260858	140581.900855	6544.044688	188.217937	4257.996619	102255.125333
min	552.000000	17.000000	28.000000	0.000000	0.000000	0.000000	507.000000
25%	76169.250000	2294.500000	19809.500000	1586.500000	74.750000	1466.500000	31378.500000
50%	124300.000000	5338.000000	84736.500000	5058.500000	191.500000	2420.000000	55804.500000
75%	413775.250000	15678.250000	194906.000000	12324.250000	339.500000	6625.500000	203191.000000
max	900059.000000	26518.000000	524891.000000	24675.000000	1651.000000	15882.000000	348650.000000

Total Confirmed Cases

This section displays the total number of confirmed cases. To view different types of graph, choose toggle the dropdown. To view different continents, please click on the names. This is an interactive graph.

display(fig1bDD)
fig1b.show()

Most continents are hit hard by the virus except for Africa and Oceania. This, however, may be under a false impression, as countries in Africa and Oceania may not have enough testing resources and facilities.

Daily Confirmed Cases

This section displays the daily number of confirmed cases. To view different continents, please click on the names. This is an interactive graph.

fig2b.show()

Total Death Cases

This section displays the total number of death cases. To view different types of graph, choose toggle the dropdown. To view different continents, please click on the names. This is an interactive graph.

display(fig3bDD)
fig3b.show()

Although North America was hit hardest with the virus, Europe has the highest deaths count. On May 22, the difference between Europe and North America in terms of total dealth cases are approximate 54,000 cases.

Daily Death Cases

This section displays the total number of daily death cases. To view different continents, please click on the names. This is an interactive graph.

fig4b.show()

Total Recovered Cases

This section displays the total number of recovered cases. To view different types of graph, choose toggle the dropdown. To view different continents, please click on the names. This is an interactive graph.

display(fig5bDD)
fig5b.show()

Daily Recovered Cases

This section displays the daily number of recovered cases. To view different continents, please click on the names. This is an interactive graph.

fig6b.show()

Active Cases

This section displays the total number of active cases. To view different continents, please click on the names. This is an interactive graph.

fig7b.show()

China

In this section, we take a look at the breakdown of the coronavirus cases in China by province, as opposed to as a whole country in previous sections.

####################################################### SKIP THIS CELL #######################################################

china_provinces = ['Hubei', 'Zhejiang', 'Guangdong', 'Henan', 'Hunan', 'Jiangxi', 'Anhui', 'Chongqing', 'Shandong', 'Sichuan', 'Jiangsu', 'Beijing', 'Shanghai', 'Fujian', 'Guangxi', 'Shaanxi', 'Yuunan', 'Hebei', 'Heilongjiang', 'Hainan', 'Liaoning', 'Shanxi', 'Tianjin', 'Guizhou', 'Gansu', 'Ningxia', 'Inner Mongolia', 'Xinjiang', 'Qinghai', 'Tibet']

china_provinces_DB = {}

for province in china_provinces:
    china_provinces_DB[province] = getProvince(fullCleanDataset, province)
    
################################################################################################################################

chinaDD = widgets.Dropdown(
    options = china_provinces,
    description = 'Provinces: ',
)

def on_change_china(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(chinaDD)
        df = getProvince(fullCleanDataset, change['new'])
        df = convertToDF(df)
        display(df.describe())
#         toggleCode(tog)

chinaDD.observe(on_change_china)
################################################################################################################################

##function

def plotGraph(fig, db, list, index, chart):
    if chart == 'lines':
        for element in list:
            addTrace(fig, element, db[element], index, chart)
    elif chart == 'bar':
        count = 0
        for element in list:
            if count == 0:
                addTrace(fig, element, db[element], index, chart)
            else:
                addTrace(fig, element, db[element], index, chart, 'legendonly')
            count = count + 1


x_dates = pd.date_range(start="01-22-2020",end="05-22-2020").to_pydatetime()
for i in range(len(x_dates)):
    x_dates[i] = x_dates[i].strftime("%b %d")

def addTrace(fig, province, dataset, index, chart, visible=True):
    if chart != 'bar':
        fig.add_trace(go.Scatter(
            name = province,
            x = x_dates,
            y = dataset[:, index],
            mode = chart,
        ))
    else:
        fig.add_trace(go.Bar(
            name = province,
            x = x_dates,
            y = dataset[:, index],
            visible = visible,
        ))

chinaProvinceDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig8_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(chinaProvinceDD)
        fig8.update_yaxes(type = change['new'])
        fig8.show()
chinaProvinceDD.observe(fig8_on_change)

fig8 = go.Figure()
plotGraph(fig8, china_provinces_DB, china_provinces, 0, 'lines')
fig8.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig8.update_layout(
    title = "Total Confirmed Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)

fig9 = go.Figure()
plotGraph(fig9, china_provinces_DB, china_provinces, 3, 'bar')
fig9.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig9.update_layout(
    title = "Daily Confirmed Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Daily Confirmed Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)

chinaProvinceDeathDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig10_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(chinaProvinceDeathDD)
        fig10.update_yaxes(type = change['new'])
        fig10.show()


chinaProvinceDeathDD.observe(fig10_on_change)

fig10 = go.Figure()
plotGraph(fig10, china_provinces_DB, china_provinces, 1, 'lines')
fig10.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig10.update_layout(
    title = "Total Deaths Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Total Deaths Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)

fig11= go.Figure()
plotGraph(fig11, china_provinces_DB, china_provinces, 4, 'bar')
fig11.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig11.update_layout(
    title = "Daily Deaths Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Daily Deaths Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)

chinaProvinceRecDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig12on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(chinaProvinceRecDD)
        fig12.update_yaxes(type = change['new'])
        fig12.show()


chinaProvinceRecDD.observe(fig12on_change)

fig12 = go.Figure()
plotGraph(fig12, china_provinces_DB, china_provinces, 2, 'lines')
fig12.update_xaxes(tickangle=315, tickfont=dict(size=13))

fig12.update_layout(
    title = "Total Recovered Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Total Recovered Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)


fig13 = go.Figure()
plotGraph(fig13, china_provinces_DB, china_provinces, 5, 'bar')
fig13.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig13.update_layout(
    title = "Daily Recovered Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Daily Recovered Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)

fig14 = go.Figure()

plotGraph(fig14, china_provinces_DB, china_provinces, 6, 'lines')
fig14.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig14.update_layout(
    title = "Active Cases in China",
    xaxis_title = "Dates",
    yaxis_title = "Active Cases",
    legend_title_text = 'Province',
    hovermode = "x unified",
)
display()

Statistical Data

Please select different provinces in the toggle to view different statistics. However, the descriptive statistics for total confirmed cases, total deaths cases and total recovered cases does not provide valuable information as they are aggregrated data.

display(chinaDD)
df = getProvince(fullCleanDataset, 'Hubei')
df = convertToDF(df)
display(df.describe())

	Total Confirmed	Total Deaths	Total Recovered	Daily Confirmed	Daily Deaths	Daily Recovered	Active
count	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000
mean	57170.737705	2902.713115	42776.122951	554.844262	36.844262	527.926230	11739.688525
std	21463.038794	1434.034149	26289.795571	1626.656308	122.725187	779.368511	16216.707736
min	444.000000	17.000000	28.000000	0.000000	0.000000	0.000000	0.000000
25%	63017.500000	2194.500000	12735.500000	0.000000	0.000000	0.000000	98.250000
50%	67800.000000	3148.500000	59657.500000	0.000000	5.000000	92.000000	2287.500000
75%	68128.000000	4512.000000	64435.000000	312.750000	35.500000	852.750000	20379.250000
max	68135.000000	4512.000000	64435.000000	14840.000000	1290.000000	3418.000000	50633.000000

Total Confirmed Cases

This section displays the total number of confirmed cases. To view different types of graph, choose toggle the dropdown. To view different provinces, please click on the names. This is an interactive graph.

display(chinaProvinceDD)
fig8.show()

This graph has shown that most of the cases from China are actually coming from Hubei. The line pattern of Hubei also resemble the line pattern of China as a whole country. When comparing Hubei and other province, other provinces is not significant at all.

Daily Confirmed Cases

This section displays the daily number of confirmed cases. To view different provinces, please click on the names. This is an interactive graph.

fig9.show()

Total Death Cases

This section displays the total number of death cases. To view different types of graph, choose toggle the dropdown. To view different provinces, please click on the names. This is an interactive graph.

display(chinaProvinceDeathDD)
fig10.show()

Daily Death Cases

This section displays the daily number of death cases. To view different provinces, please click on the names. This is an interactive graph.

fig11.show()

Total Recovered Cases

This section displays the total number of recovered cases. To view different types of graph, choose toggle the dropdown. To view different provinces, please click on the names. This is an interactive graph.

display(chinaProvinceRecDD)
fig12.show()

The number of recovery cases in Hubei is extraordinarily high because most infected cases are from Hubei. When Hubei is toggled off, the highest number of recovery cases occured in Guangdong. This could be due to a number of reasons: stricter precautionary measures placed by local authorities, better quality of medical service, and density of area.

Daily Recovered Cases

This section displays the daily number of recovered cases. To view different provinces, please click on the names. This is an interactive graph.

fig13.show()

Active Cases

This section displays the total number of active cases. To view different provinces, please click on the names. This is an interactive graph.

fig14.show()

Number of active cases have been dramatically decreased by early April, thanks to strict movement restrictions put in place.

USA

In this section, we take a look at the breakdown of the coronavirus cases in US by state, as opposed to as a whole country in previous sections.

####################################################### SKIP THIS CELL #######################################################

US_States = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Columbia', 
             'Florida', 'Georgia', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 
             'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 
             'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 
             'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 
             'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 
             'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']
US_States_DB = {}

for state in US_States:
    US_States_DB[state] = getState(fullCleanDataset, state)
    
################################################################################################################################

usDD = widgets.Dropdown(
    options = US_States,
    description = 'States: ',
)

def on_change_US(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(usDD)
        df = getState(fullCleanDataset, change['new'])
        df = convertToDF(df)
        display(df.describe())
#         toggleCode(tog)

usDD.observe(on_change_US)
################################################################################################################################

##function

def plotGraph(fig, db, list, index, chart):
    if chart == 'lines':
        for element in list:
            addTrace(fig, element, db[element], index, chart)
    elif chart == 'bar':
        count = 0
        for element in list:
            if count == 0:
                addTrace(fig, element, db[element], index, chart)
            else:
                addTrace(fig, element, db[element], index, chart, 'legendonly')
            count = count + 1


x_dates = pd.date_range(start="01-22-2020",end="05-22-2020").to_pydatetime()
for i in range(len(x_dates)):
    x_dates[i] = x_dates[i].strftime("%b %d")

def addTrace(fig, province, dataset, index, chart, visible=True):
    if chart != 'bar':
        fig.add_trace(go.Scatter(
            name = province,
            x = x_dates,
            y = dataset[:, index],
            mode = chart,
        ))
    else:
        fig.add_trace(go.Bar(
            name = province,
            x = x_dates,
            y = dataset[:, index],
            visible = visible,
        ))

usStateDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig15_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(usStateDD)
        fig15.update_yaxes(type = change['new'])
        fig15.show()
#         toggleCode(tog)

usStateDD.observe(fig15_on_change)



fig15 = go.Figure()

plotGraph(fig15, US_States_DB, US_States, 0, 'lines')
fig15.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig15.update_layout(
    title = "Total Confirmed Cases in the US",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)


fig16 = go.Figure()
plotGraph(fig16, US_States_DB, US_States, 3, 'bar')
fig16.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig16.update_layout(
    title = "Daily Confirmed Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Daily Confirmed Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)

usStateDeathDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig17_on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(usStateDeathDD)
        fig17.update_yaxes(type = change['new'])
        fig17.show()


usStateDeathDD.observe(fig17_on_change)

fig17 = go.Figure()
plotGraph(fig17, US_States_DB, US_States, 1, 'lines')
fig17.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig17.update_layout(
    title = "Total Deaths Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Total Deaths Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)

fig18= go.Figure()
plotGraph(fig18, US_States_DB, US_States, 4, 'bar')
fig18.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig18.update_layout(
    title = "Daily Deaths Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Daily Deaths Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)

usStateRecDD = widgets.Dropdown(
    options = [('linear', 'linear'), ('logarithmic', 'log')],
    description = 'Graph type: ',
)

def fig19on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        clear_output()
        display(usStateRecDD)
        fig19.update_yaxes(type = change['new'])
        fig19.show()


usStateRecDD.observe(fig19on_change)

fig19 = go.Figure()
plotGraph(fig19, US_States_DB, US_States, 2, 'lines')
fig19.update_xaxes(tickangle=315, tickfont=dict(size=13))

fig19.update_layout(
    title = "Total Recovered Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Total Recovered Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)


fig20 = go.Figure()
plotGraph(fig20, US_States_DB, US_States, 5, 'bar')
fig20.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig20.update_layout(
    title = "Daily Recovered Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Daily Recovered Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)

fig21 = go.Figure()

plotGraph(fig21, US_States_DB, US_States, 6, 'lines')
fig21.update_xaxes(tickangle=315, tickfont=dict(size=13))
fig21.update_layout(
    title = "Active Cases in US",
    xaxis_title = "Dates",
    yaxis_title = "Active Cases",
    legend_title_text = 'State',
    hovermode = "x unified",
)

display()

Statistical Data

Please select different states in the toggle to view different statistics. However, the descriptive statistics for total confirmed cases, total deaths cases and total recovered cases does not provide valuable information as they are aggregrated data.

display(usDD)
df = getState(fullCleanDataset, 'Alabama')
df = convertToDF(df)
display(df.describe())

	Total Confirmed	Total Deaths	Total Recovered	Daily Confirmed	Daily Deaths	Daily Recovered	Active
count	122.000000	122.000000	122.0	122.000000	122.000000	122.0	122.000000
mean	2930.827869	108.319672	0.0	112.049180	4.434426	0.0	2822.516393
std	4071.523362	163.476349	0.0	135.223467	6.931766	0.0	3908.905742
min	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000
25%	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000
50%	176.500000	0.000000	0.0	35.500000	0.000000	0.0	176.500000
75%	5524.000000	192.750000	0.0	220.500000	6.750000	0.0	5331.250000
max	13670.000000	541.000000	0.0	676.000000	32.000000	0.0	13129.000000

Total Confirmed Cases

This section displays the total number of confirmed cases. To view different types of graph, choose toggle the dropdown. To view different states, please click on the names. This is an interactive graph.

display(usStateDD)
fig15.show()

New York is hit the hardest. This is because it is a metropolitan city, and most residents take the bus or the subway, which is a great breeding ground for viruses, especially during peak hours when places are packed. The US residents have also shown much reisstance to wearing a mask and this has resulted in a spike of confirmed cases as there is no layer of protection between people.

Daily Confirmed Cases

This section displays the daily number of confirmed cases. To view different states, please click on the names. This is an interactive graph.

fig16.show()

Total Death Cases

This section displays the total number of death cases. To view different types of graph, choose toggle the dropdown. To view different states, please click on the names. This is an interactive graph.

display(usStateDeathDD)
fig17.show()

Daily Death Cases

This section displays the daily number of death cases. To view different states, please click on the names. This is an interactive graph.

fig18.show()

Total Recovered Cases

This section displays the total number of recovered cases. To view different types of graph, choose toggle the dropdown. To view different states, please click on the names. This is an interactive graph.

display(usStateRecDD)
fig19.show()

The highest number of recovered cases shown in the graph is 6. This is because the dataset we used have recorded the recovery cases in country level starting from the March. Hence, we could not deduct any useful information from this graph. For total number of recovered cases, please refer back to graph shown in Top Countries section.

Daily Recovered Cases

This section displays the daily number of recovered cases. To view different states, please click on the names. This is an interactive graph.

fig20.show()

Since the recovery cases is not recoreded at state level. We could not compute the daily recovery cases as well. For daily number of recovered cases, please refer back to graph shown in Top Countries section.

Active Cases

This section displays the total number of active cases. To view different states, please click on the names. This is an interactive graph.

fig21.show()

None of the states in the US show signs of flattening the curve.

USA vs China

This section elaborates on the comparison between the USA and China, two of the biggest forces in the world. In this section we may be able to see the difference between the method of restriction and the results obtained.

####################################################### SKIP THIS CELL #######################################################

US = getCountry(fullCleanDataset,'US')
China = getCountry(fullCleanDataset,'China')

fig22 = go.Figure()
addTrace(fig22, 'US', US, 0, 'lines')
addTrace(fig22, 'China', China, 0, 'lines')

fig22.update_layout(
    title = "USA vs China Total Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'Country',
    hovermode = "x unified",
)

fig22.update_xaxes(tickangle=315, tickfont=dict(size=13))

shanghai = getProvince(fullCleanDataset,'Shanghai')
florida = getState(fullCleanDataset,'Florida')

fig23 = go.Figure()
addTrace(fig23, 'Florida', florida, 0, 'lines')
addTrace(fig23, 'Shanghai', shanghai, 0, 'lines')

fig23.update_layout(
    title = "Most Dense Areas Total Confirmed Cases",
    xaxis_title = "Dates",
    yaxis_title = "Total Confirmed Cases",
    legend_title_text = 'State/Province',
    hovermode = "x unified",
)
fig23.update_xaxes(tickangle=315, tickfont=dict(size=13))
display()

Total Confirmed Cases (Country Level)

fig22.show()

The number of confirmed cases in the USA exceeds China by many times even though they were hit much later. Just when China was about to flatten the curve, the numbers in the USA is flying off the charts. Restriction measures were only in place by April, which by then had already been too late to contain the virus.

Total Confirmed Cases (Densest State/Province Level)

fig23.show()

To make a fairer comparison, we have compared the densest areas of each country to see how they fare. Shanghai and Florida are both chosen and compared against each other. Numbers in Shanghai have been kept relatively low, not even into the thousands, yet Florida does not seem to be doing very well. This also proven that population density does not have a huge impact on the number of confirmed cases. Instead, the actions taken by the goverments and citizen is the key of controlling the virus.

Malaysia

An overview of cases in Malaysia.

####################################################### SKIP THIS CELL #######################################################

malaysia = getCountry(fullCleanDataset, "Malaysia")

figMsia = go.Figure()
addTrace(figMsia, 'Total Confirmed Cases', malaysia, 0, 'lines')
addTrace(figMsia, 'Total Deaths Cases', malaysia, 1, 'lines')
addTrace(figMsia, 'Total Recovered Cases', malaysia, 2, 'lines')
addTrace(figMsia, 'Active Cases', malaysia, 6, 'lines')
figMsia.update_xaxes(tickangle=315, tickfont=dict(size=13))
figMsia.update_layout(
    title = "Malaysia (Line)",
    xaxis_title = "Dates",
    yaxis_title = "Number of Cases",
    legend_title_text = '',
    hovermode = "x unified",
)


figMsia2 = go.Figure()
addTrace(figMsia2, 'Daily Confirmed Cases', malaysia, 3, 'bar')
addTrace(figMsia2, 'Daily Deaths Cases', malaysia, 4, 'bar')
addTrace(figMsia2, 'Daily Recovered Cases', malaysia, 5, 'bar')
figMsia2.update_xaxes(tickangle=315, tickfont=dict(size=13))
figMsia2.update_layout(
    title = "Malaysia (Bar)",
    xaxis_title = "Dates",
    yaxis_title = "Number of Cases",
    legend_title_text = '',
    hovermode = "x unified",
)

display()

Statistical Data

The descriptive statistics for total confirmed cases, total deaths cases and total recovered cases does not provide valuable information as they are aggregrated data.

df = convertToDF(malaysia)
display(df.describe())

	Total Confirmed	Total Deaths	Total Recovered	Daily Confirmed	Daily Deaths	Daily Recovered	Active
count	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000	122.000000
mean	2623.032787	41.516393	1579.295082	58.500000	0.942623	48.024590	1002.221311
std	2753.765260	46.367065	2052.293535	64.138313	1.517388	57.215623	952.491127
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	22.000000	0.000000	15.000000	1.000000	0.000000	0.000000	12.500000
50%	1412.000000	12.000000	149.000000	37.500000	0.000000	18.000000	1153.000000
75%	5519.500000	92.750000	3426.250000	108.500000	1.000000	88.000000	1813.250000
max	7137.000000	115.000000	5859.000000	235.000000	8.000000	236.000000	2596.000000

Total and Active Cases

To view different line chart, please click on the names. This is an interactive graph.

display(figMsia)

This graph combines total confirmed, death, recovered and active cases in a line graph form. Malaysia seems to have done a great job in containing the virus as total active cases have been on a steady decline.

Daily Cases

To view different bar chart, please click on the names. This is an interactive graph.

display(figMsia2)

This graph combines total confirmed, death, recovered and active cases in a bar graph form.

Prediction

We have tried to performed some regression prediction with scikit-learn library. First, we reshape the data into the format that is acceptable by the machine learning model. We also initialised the machine learning model with a set of parameters. Then, we trained the model and let the model to decide what is the best parameters. Finally, we use the model to predict the number of cases. However, the training process is repeated for each prediction. The accurary score is also shown for each prediction.

def predict(countryDataset, index, title, futureDays):
    ## get dates for actual data and prediction
    x_dates = pd.date_range(start="01-22-2020",end="05-22-2020").to_pydatetime()
    x_dates2 = pd.date_range(start="01-22-2020", periods=(len(dates)+futureDays)).to_pydatetime()
    for i in range(len(x_dates)):
        x_dates[i] = x_dates[i].strftime("%b %d")
    for i in range(len(x_dates2)):
        x_dates2[i] = x_dates2[i].strftime("%b %d")

    ## for actual data    
    X = np.arange(len(x_dates)).reshape(-1, 1)
    y = countryDataset[:len(x_dates), index]

    ## for prediction
    X2 = np.arange(len(dates)+futureDays).reshape(-1, 1)
    
    # SVM parameters
    kernel = ['rbf']
    c = [0.01, 0.1, 1, 10, 100, 500, 750, 1000, 1250]
    gamma = [0.01, 0.1, 1]
    epsilon = [0.01, 0.1, 1]
    shrinking = [True, False]
    svm_grid = {'kernel':kernel, 'C':c, 'gamma':gamma, 'epsilon':epsilon, 'shrinking':shrinking}

    svm = SVR()
    svm_search = sklearn.model_selection.RandomizedSearchCV(svm, svm_grid, scoring = 'neg_mean_squared_error', 
                                                            cv=3, return_train_score=True, n_jobs=1, n_iter=50, verbose=1)
    svm_search.fit(X, y)
    
    
    # predict with best parameters
    svm_best = svm_search.best_estimator_
    y_pred = svm_best.predict(X).astype(int)
    score = accuracy_score(y, y_pred, normalize=True)
    y_pred = svm_best.predict(X2).astype(int)
    
    pred_fig = go.Figure()

    pred_fig.add_trace(go.Scatter(
            name = 'Actual cases',
            x = x_dates2,
            y = y,
            mode = 'lines',
        ))

    pred_fig.add_trace(go.Scatter(
            name = 'Predicted cases',
            x = x_dates2,
            y = y_pred,
            mode = 'lines',
        ))

    pred_fig.update_layout(
        title = title + " (Actual vs Prediction)",
        xaxis_title = "Dates",
        yaxis_title = title,
        legend_title_text = '',
        hovermode = "x unified",
    )
    pred_fig.update_xaxes(tickangle=315, tickfont=dict(size=13))
    
    clear_output()
    print("svm parameter: ")
    print(svm_best)
    print("Accuracy: ")
    print(score)
    pred_fig.show()
    

predict(malaysia, 0, "Total Confirmed Cases", 30)

svm parameter: 
SVR(C=1250, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.01,
    kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)
Accuracy: 
0.11475409836065574

predict(malaysia, 1, "Daily Confirmed Cases", 30)

svm parameter: 
SVR(C=750, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.01,
    kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)
Accuracy: 
0.6721311475409836

predict(malaysia, 6, "Active Cases", 30)

svm parameter: 
SVR(C=1250, cache_size=200, coef0=0.0, degree=3, epsilon=1, gamma=0.01,
    kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)
Accuracy: 
0.10655737704918032

This prediction is only done in Malaysia, not on other countries. All predictions show a decreasing trend, which has proven to be true when compared with real cases reported through June.

However, the algorithm used to predict total number of confirmed cases seem to provide a false result as the total number of confirmed cases should not be decreasing, seeing that cases are being added every day. Malaysia has recently achieved 0 number of daily new cases in July.